{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "32e5d6f2",
   "metadata": {},
   "source": [
    "## Exploring a Mean Reversion Strategy with Zipline Reloaded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3328e238",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e7c3236c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pandas_datareader as web\n",
    "from IPython.display import Markdown, display\n",
    "from zipline import run_algorithm\n",
    "from zipline.api import (\n",
    "    attach_pipeline,\n",
    "    calendars,\n",
    "    date_rules,\n",
    "    get_datetime,\n",
    "    get_open_orders,\n",
    "    order_target_percent,\n",
    "    pipeline_output,\n",
    "    record,\n",
    "    schedule_function,\n",
    "    set_commission,\n",
    "    set_slippage,\n",
    "    time_rules,\n",
    ")\n",
    "from zipline.finance import commission, slippage\n",
    "from zipline.pipeline import Pipeline\n",
    "from zipline.pipeline.data import USEquityPricing\n",
    "from zipline.pipeline.factors import AverageDollarVolume, CustomFactor, Returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b8aeb006",
   "metadata": {},
   "outputs": [],
   "source": [
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "89e81e71",
   "metadata": {},
   "outputs": [],
   "source": [
    "N_LONGS = N_SHORTS = 50\n",
    "MONTH = 21\n",
    "YEAR = 12 * MONTH"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c14adb6",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "Define a custom mean reversion factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4fb04783",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "class MeanReversion(CustomFactor):\n",
    "    inputs = [Returns(window_length=MONTH)]\n",
    "    window_length = YEAR\n",
    "\n",
    "    def compute(self, today, assets, out, monthly_returns):\n",
    "        df = pd.DataFrame(monthly_returns)\n",
    "        out[:] = df.iloc[-1].sub(df.mean()).div(df.std())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97604149",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "Define the pipeline for factors and securities selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "04699d2d",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "def make_pipeline():\n",
    "    mean_reversion = MeanReversion()\n",
    "    dollar_volume = AverageDollarVolume(window_length=30)\n",
    "    return Pipeline(\n",
    "        columns={\n",
    "            \"longs\": mean_reversion.bottom(N_LONGS),\n",
    "            \"shorts\": mean_reversion.top(N_SHORTS),\n",
    "            \"ranking\": mean_reversion.rank(ascending=False),\n",
    "        },\n",
    "        screen=dollar_volume.top(100),\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f24e4342",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "Define the function to be called before the trading starts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "01e7ff94",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "def before_trading_start(context, data):\n",
    "    context.factor_data = pipeline_output(\"factor_pipeline\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60ac1731",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "Define the initialize function to set up the algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d672ad15",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "def initialize(context):\n",
    "    attach_pipeline(make_pipeline(), \"factor_pipeline\")\n",
    "    schedule_function(\n",
    "        rebalance,\n",
    "        date_rules.week_start(),\n",
    "        time_rules.market_open(),\n",
    "        calendar=calendars.US_EQUITIES,\n",
    "    )\n",
    "\n",
    "    set_commission(us_equities=commission.PerShare(cost=0.00075, min_trade_cost=0.01))\n",
    "\n",
    "    set_slippage(\n",
    "        us_equities=slippage.VolumeShareSlippage(volume_limit=0.0025, price_impact=0.01)\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76d0a97d",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "Define the rebalance function to execute trades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5d23a489",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "def rebalance(context, data):\n",
    "    factor_data = context.factor_data\n",
    "    record(factor_data=factor_data.ranking)\n",
    "\n",
    "    assets = factor_data.index\n",
    "    record(prices=data.current(assets, \"price\"))\n",
    "\n",
    "    longs = assets[factor_data.longs]\n",
    "    shorts = assets[factor_data.shorts]\n",
    "    divest = set(context.portfolio.positions.keys()) - set(longs.union(shorts))\n",
    "    print(\n",
    "        f\"{get_datetime().date()} | Longs {len(longs)} | Shorts | {len(shorts)} | {context.portfolio.portfolio_value}\"\n",
    "    )\n",
    "\n",
    "    exec_trades(data, assets=divest, target_percent=0)\n",
    "    exec_trades(data, assets=longs, target_percent=1 / N_LONGS)\n",
    "    exec_trades(data, assets=shorts, target_percent=-1 / N_SHORTS)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e89f64e",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "Define the function to execute trades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ff944166",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "def exec_trades(data, assets, target_percent):\n",
    "    for asset in assets:\n",
    "        if data.can_trade(asset) and not get_open_orders(asset):\n",
    "            order_target_percent(asset, target_percent)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a675f9d",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "Define the analyze function to plot performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a042f12a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def analyze(context, perf):\n",
    "    perf.portfolio_value.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bf49fe8",
   "metadata": {},
   "source": [
    "Define the start and end dates for the backtest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5e9d82c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "start = pd.Timestamp(\"2016\")\n",
    "end = pd.Timestamp(\"2018\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d152f46",
   "metadata": {},
   "source": [
    "Fetch the benchmark returns (S&P 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fe9b7dc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "sp500 = web.DataReader(\"SP500\", \"fred\", start, end).SP500\n",
    "benchmark_returns = sp500.pct_change()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65fb0e68",
   "metadata": {},
   "source": [
    "Run the algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2c33526d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2016-01-04 | Longs 0 | Shorts | 1 | 100000.0\n",
      "2016-01-11 | Longs 0 | Shorts | 1 | 99993.16349999886\n",
      "2016-01-19 | Longs 2 | Shorts | 1 | 100021.36374999832\n",
      "2016-01-25 | Longs 2 | Shorts | 2 | 100047.47224999769\n",
      "2016-02-01 | Longs 3 | Shorts | 3 | 99889.9749999967\n",
      "2016-02-08 | Longs 2 | Shorts | 2 | 99616.54649999554\n",
      "2016-02-16 | Longs 2 | Shorts | 0 | 99849.90549999391\n",
      "2016-02-22 | Longs 1 | Shorts | 0 | 100195.42449999285\n",
      "2016-02-29 | Longs 1 | Shorts | 0 | 100155.76424999181\n",
      "2016-03-07 | Longs 2 | Shorts | 1 | 100114.18424999181\n",
      "2016-03-14 | Longs 0 | Shorts | 0 | 100304.28374999072\n",
      "2016-03-21 | Longs 1 | Shorts | 2 | 99210.60974998782\n",
      "2016-03-28 | Longs 1 | Shorts | 0 | 99355.18649998584\n",
      "2016-04-04 | Longs 1 | Shorts | 0 | 99108.98424998432\n",
      "2016-04-11 | Longs 2 | Shorts | 1 | 99483.63424998433\n",
      "2016-04-18 | Longs 2 | Shorts | 0 | 99556.3877499843\n",
      "2016-04-25 | Longs 2 | Shorts | 0 | 99573.0884999835\n",
      "2016-05-02 | Longs 4 | Shorts | 0 | 99557.5844999825\n",
      "2016-05-09 | Longs 3 | Shorts | 1 | 99399.40899998133\n",
      "2016-05-16 | Longs 3 | Shorts | 2 | 99355.67949998101\n",
      "2016-05-23 | Longs 3 | Shorts | 2 | 99271.2524999799\n",
      "2016-05-31 | Longs 1 | Shorts | 2 | 99356.98699997793\n",
      "2016-06-06 | Longs 4 | Shorts | 3 | 99366.66949997128\n",
      "2016-06-13 | Longs 3 | Shorts | 1 | 99543.62649996586\n",
      "2016-06-20 | Longs 2 | Shorts | 1 | 99459.02499996335\n",
      "2016-06-27 | Longs 4 | Shorts | 1 | 98925.75674996113\n",
      "2016-07-05 | Longs 0 | Shorts | 3 | 99260.76674996056\n",
      "2016-07-11 | Longs 3 | Shorts | 2 | 99335.8389999511\n",
      "2016-07-18 | Longs 1 | Shorts | 1 | 99421.94524994826\n",
      "2016-07-25 | Longs 0 | Shorts | 2 | 99641.29024993867\n",
      "2016-08-01 | Longs 0 | Shorts | 2 | 99574.93024993711\n",
      "2016-08-08 | Longs 1 | Shorts | 2 | 99451.26299993655\n",
      "2016-08-15 | Longs 1 | Shorts | 0 | 99399.90774993491\n",
      "2016-08-22 | Longs 2 | Shorts | 0 | 99388.73049993235\n",
      "2016-08-29 | Longs 3 | Shorts | 0 | 99375.95774993206\n",
      "2016-09-06 | Longs 4 | Shorts | 1 | 99240.71024993109\n",
      "2016-09-12 | Longs 6 | Shorts | 0 | 99284.24874992917\n",
      "2016-09-19 | Longs 3 | Shorts | 0 | 99310.75224992516\n",
      "2016-09-26 | Longs 2 | Shorts | 1 | 99342.7364999232\n",
      "2016-10-03 | Longs 2 | Shorts | 0 | 99432.89149991871\n",
      "2016-10-10 | Longs 1 | Shorts | 0 | 99534.435249917\n",
      "2016-10-17 | Longs 2 | Shorts | 1 | 99567.15024991655\n",
      "2016-10-24 | Longs 1 | Shorts | 4 | 99701.46224991114\n",
      "2016-10-31 | Longs 2 | Shorts | 4 | 99758.98474989916\n",
      "2016-11-07 | Longs 1 | Shorts | 2 | 99690.08774989613\n",
      "2016-11-14 | Longs 2 | Shorts | 0 | 99684.02199989371\n",
      "2016-11-21 | Longs 3 | Shorts | 0 | 99687.2547498928\n",
      "2016-11-28 | Longs 6 | Shorts | 0 | 99699.0027498928\n",
      "2016-12-05 | Longs 5 | Shorts | 1 | 99579.59924989115\n",
      "2016-12-12 | Longs 6 | Shorts | 0 | 99907.10849989054\n",
      "2016-12-19 | Longs 1 | Shorts | 0 | 100016.05374988737\n",
      "2016-12-27 | Longs 0 | Shorts | 0 | 99994.40949988627\n",
      "2017-01-03 | Longs 2 | Shorts | 1 | 99995.46924988582\n",
      "2017-01-09 | Longs 0 | Shorts | 2 | 99969.06774988303\n",
      "2017-01-17 | Longs 1 | Shorts | 2 | 99992.54574988167\n",
      "2017-01-23 | Longs 2 | Shorts | 1 | 100009.24349988031\n",
      "2017-01-30 | Longs 1 | Shorts | 3 | 100019.00799987803\n",
      "2017-02-06 | Longs 2 | Shorts | 4 | 100004.74024987646\n",
      "2017-02-13 | Longs 1 | Shorts | 4 | 100064.67049987541\n",
      "2017-02-21 | Longs 1 | Shorts | 0 | 100071.47524986957\n",
      "2017-02-27 | Longs 2 | Shorts | 6 | 100130.77874986651\n",
      "2017-03-06 | Longs 1 | Shorts | 4 | 100086.32224986328\n",
      "2017-03-13 | Longs 1 | Shorts | 4 | 99994.76399985905\n",
      "2017-03-20 | Longs 0 | Shorts | 2 | 100120.12849985655\n",
      "2017-03-27 | Longs 1 | Shorts | 1 | 100126.64024985455\n",
      "2017-04-03 | Longs 3 | Shorts | 0 | 100107.37074985116\n",
      "2017-04-10 | Longs 6 | Shorts | 2 | 100145.07774984317\n",
      "2017-04-17 | Longs 6 | Shorts | 2 | 100180.71524983783\n",
      "2017-04-24 | Longs 2 | Shorts | 3 | 100464.08749983532\n",
      "2017-05-01 | Longs 1 | Shorts | 4 | 100561.82099982862\n",
      "2017-05-08 | Longs 1 | Shorts | 5 | 100368.84449982464\n",
      "2017-05-15 | Longs 1 | Shorts | 2 | 100186.15274981526\n",
      "2017-05-22 | Longs 0 | Shorts | 3 | 100150.98049981466\n",
      "2017-05-30 | Longs 1 | Shorts | 2 | 100135.34049981223\n",
      "2017-06-05 | Longs 3 | Shorts | 2 | 100122.40974981069\n",
      "2017-06-12 | Longs 4 | Shorts | 0 | 100296.96449980921\n",
      "2017-06-19 | Longs 3 | Shorts | 2 | 100208.68149980347\n",
      "2017-06-26 | Longs 5 | Shorts | 4 | 100103.75849980078\n",
      "2017-07-03 | Longs 9 | Shorts | 5 | 100345.95199979268\n",
      "2017-07-10 | Longs 8 | Shorts | 5 | 100121.36274979003\n",
      "2017-07-17 | Longs 4 | Shorts | 5 | 100483.58899978599\n",
      "2017-07-24 | Longs 5 | Shorts | 2 | 100115.06224977087\n",
      "2017-07-31 | Longs 2 | Shorts | 6 | 100449.19574976567\n",
      "2017-08-07 | Longs 1 | Shorts | 6 | 100488.33099976288\n",
      "2017-08-14 | Longs 1 | Shorts | 4 | 100579.2624997609\n",
      "2017-08-21 | Longs 1 | Shorts | 1 | 100653.2324997606\n",
      "2017-08-28 | Longs 3 | Shorts | 1 | 100627.89124975448\n",
      "2017-09-05 | Longs 1 | Shorts | 1 | 100743.42449975121\n",
      "2017-09-11 | Longs 2 | Shorts | 3 | 100798.43974974885\n",
      "2017-09-18 | Longs 4 | Shorts | 1 | 101008.80124974706\n",
      "2017-09-25 | Longs 5 | Shorts | 1 | 101256.9089997437\n",
      "2017-10-02 | Longs 4 | Shorts | 0 | 101395.06399974026\n",
      "2017-10-09 | Longs 3 | Shorts | 3 | 101545.01899973433\n",
      "2017-10-16 | Longs 3 | Shorts | 3 | 101508.26249972888\n",
      "2017-10-23 | Longs 4 | Shorts | 2 | 101593.4162497248\n",
      "2017-10-30 | Longs 9 | Shorts | 7 | 101234.84999972026\n",
      "2017-11-06 | Longs 6 | Shorts | 6 | 101138.68499971826\n",
      "2017-11-13 | Longs 4 | Shorts | 4 | 100804.24474971641\n",
      "2017-11-20 | Longs 4 | Shorts | 2 | 100692.08324971468\n",
      "2017-11-27 | Longs 3 | Shorts | 2 | 100687.88624971322\n",
      "2017-12-04 | Longs 3 | Shorts | 1 | 100996.10474970903\n",
      "2017-12-11 | Longs 1 | Shorts | 2 | 101074.34449970587\n",
      "2017-12-18 | Longs 4 | Shorts | 1 | 101052.60499970303\n",
      "2017-12-26 | Longs 4 | Shorts | 4 | 100838.68299969957\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "perf = run_algorithm(\n",
    "    start=start,\n",
    "    end=end,\n",
    "    initialize=initialize,\n",
    "    analyze=analyze,\n",
    "    benchmark_returns=benchmark_returns,\n",
    "    before_trading_start=before_trading_start,\n",
    "    capital_base=100_000,\n",
    "    bundle=\"quandl\",\n",
    ")\n",
    "perf.to_pickle(\"mean_reversion.pickle\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a62ff7e7",
   "metadata": {},
   "source": [
    "Display the pipeline graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "995330a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
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       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 577)\">\n",
       "<title>G</title>\n",
       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-577 469,-577 469,4 -4,4\"/>\n",
       "<g id=\"clust1\" class=\"cluster\">\n",
       "<title>cluster_Output</title>\n",
       "<polygon fill=\"#ffec8b\" stroke=\"#ffec8b\" points=\"21,-8 21,-183 457,-183 457,-8 21,-8\"/>\n",
       "<text text-anchor=\"middle\" x=\"239\" y=\"-15.2\" font-family=\"Times,serif\" font-size=\"14.00\">Output</text>\n",
       "</g>\n",
       "<g id=\"clust2\" class=\"cluster\">\n",
       "<title>cluster_Input</title>\n",
       "<polygon fill=\"#ffec8b\" stroke=\"#ffec8b\" points=\"8,-467 8,-565 335,-565 335,-467 8,-467\"/>\n",
       "<text text-anchor=\"middle\" x=\"171.5\" y=\"-547.7\" font-family=\"Times,serif\" font-size=\"14.00\">Input</text>\n",
       "</g>\n",
       "<!-- 5799846496 -->\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>5799846496</title>\n",
       "<polygon fill=\"#ccebc5\" stroke=\"black\" points=\"449.38,-166.75 318.62,-166.75 318.62,-125.75 449.38,-125.75 449.38,-166.75\"/>\n",
       "<text text-anchor=\"start\" x=\"326.62\" y=\"-149.45\" font-family=\"Times,serif\" font-size=\"14.00\">Expression:</text>\n",
       "<text text-anchor=\"start\" x=\"326.62\" y=\"-132.95\" font-family=\"Times,serif\" font-size=\"14.00\">  x_0 &lt;= (5.00E+01)</text>\n",
       "</g>\n",
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       "<title>5799845248</title>\n",
       "<polygon fill=\"#ccebc5\" stroke=\"black\" points=\"304.38,-81.5 173.62,-81.5 173.62,-40.5 304.38,-40.5 304.38,-81.5\"/>\n",
       "<text text-anchor=\"start\" x=\"181.62\" y=\"-64.2\" font-family=\"Times,serif\" font-size=\"14.00\">Expression:</text>\n",
       "<text text-anchor=\"start\" x=\"181.62\" y=\"-47.7\" font-family=\"Times,serif\" font-size=\"14.00\">  x_0 &lt;= (5.00E+01)</text>\n",
       "</g>\n",
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       "<text text-anchor=\"start\" x=\"185.75\" y=\"-157.7\" font-family=\"Times,serif\" font-size=\"14.00\">Rank:</text>\n",
       "<text text-anchor=\"start\" x=\"185.75\" y=\"-141.2\" font-family=\"Times,serif\" font-size=\"14.00\">  method: 'ordinal'</text>\n",
       "<text text-anchor=\"start\" x=\"185.75\" y=\"-124.7\" font-family=\"Times,serif\" font-size=\"14.00\">  mask: AssetExists</text>\n",
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       "<title>5799845344-&gt;5799845248</title>\n",
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       "<text text-anchor=\"start\" x=\"36.62\" y=\"-132.95\" font-family=\"Times,serif\" font-size=\"14.00\">  x_0 &lt;= (1.00E+02)</text>\n",
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       "<text text-anchor=\"start\" x=\"23.75\" y=\"-498.7\" font-family=\"Times,serif\" font-size=\"14.00\">  Dataset: EquityPricing</text>\n",
       "<text text-anchor=\"start\" x=\"23.75\" y=\"-482.2\" font-family=\"Times,serif\" font-size=\"14.00\">  Column: volume</text>\n",
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       "<text text-anchor=\"start\" x=\"30.62\" y=\"-421.7\" font-family=\"Times,serif\" font-size=\"14.00\">AverageDollarVolume:</text>\n",
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       "<text text-anchor=\"start\" x=\"188.75\" y=\"-498.7\" font-family=\"Times,serif\" font-size=\"14.00\">  Dataset: EquityPricing</text>\n",
       "<text text-anchor=\"start\" x=\"188.75\" y=\"-482.2\" font-family=\"Times,serif\" font-size=\"14.00\">  Column: close</text>\n",
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       "<text text-anchor=\"start\" x=\"197.75\" y=\"-421.7\" font-family=\"Times,serif\" font-size=\"14.00\">Returns:</text>\n",
       "<text text-anchor=\"start\" x=\"197.75\" y=\"-405.2\" font-family=\"Times,serif\" font-size=\"14.00\">  window_length: 21</text>\n",
       "</g>\n",
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      "text/plain": [
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      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = make_pipeline()\n",
    "p.show_graph()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a387994a",
   "metadata": {},
   "source": [
    "Plot beta and alpha of the portfolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b2cb442e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "perf.beta.plot()\n",
    "perf.alpha.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59db3b53",
   "metadata": {},
   "source": [
    "**Jason Strimpel** is the founder of <a href='https://pyquantnews.com/'>PyQuant News</a> and co-founder of <a href='https://www.tradeblotter.io/'>Trade Blotter</a>. His career in algorithmic trading spans 20+ years. He previously traded for a Chicago-based hedge fund, was a risk manager at JPMorgan, and managed production risk technology for an energy derivatives trading firm in London. In Singapore, he served as APAC CIO for an agricultural trading firm and built the data science team for a global metals trading firm. Jason holds degrees in Finance and Economics and a Master's in Quantitative Finance from the Illinois Institute of Technology. His career spans America, Europe, and Asia. He shares his expertise through the <a href='https://pyquantnews.com/subscribe-to-the-pyquant-newsletter/'>PyQuant Newsletter</a>, social media, and has taught over 1,000+ algorithmic trading with Python in his popular course **<a href='https://gettingstartedwithpythonforquantfinance.com/'>Getting Started With Python for Quant Finance</a>**. All code is for educational purposes only. Nothing provided here is financial advise. Use at your own risk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de28c228-a01d-46af-9dde-018992b79cb1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "jupytext": {
   "cell_metadata_filter": "-all",
   "main_language": "python",
   "notebook_metadata_filter": "-all"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
